The objective of this study was to generate groups of agri-food producers with high affinity in relation to their sustainable waste management practices. The aim of conforming these groups is the development of synergies, knowledge management, and policy- and decision-making by diverse stakeholders. A survey was conducted among the most experienced farmers in the region of Nuevo Urecho, Michoacán, Mexico, and a total of eight variables relating to sustainable waste management practices, agricultural food loss, and the waste generated at each stage of the production process were examined. The retrieved data were treated using the maximum inverse correspondence algorithm and the Galois Lattice was applied to generate clusters of highly affine producers. The results indicate 163 possible elements that generate the power set, and 31 maximum inverse correspondences were obtained. At this point, it is possible to determine the maximum number of relationships, called affinities. In general, all 15 considered farmers shared the measure of revaluation of food waste and 90% of the farmers shared affinity in measures related to ecological care and the proper management of waste. A practical implication of this study is the conformation of highly affine clusters for both policy and strategic decision-making.
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Digital technologies permeate and transform organisational practices. As a society, we need means to explore the uncharted terrain that lies ahead and the desirability and consequences of possible courses of action to move forward. We investigate a design approach, called ‘future probing’, to envision and critically analyse possible futures around digital technologies. We first reconstruct our journey and describe related insights on the process, content and context level. Reflecting on the journey, we then extract a key insight revolving around the challenge for participants to link back from exploring the future to their present practice. In a first attempt at theorizing these difficulties, we see future probing as a practice that opens up adaptive space (Uhl-Bien & Arena, 2017) in which people from different backgrounds engage in dialogue about possible futures of digital technologies. We found that adaptive processes, like semi structuring, temporary decentralisation, and collaboration (Uhl-Bien & Arena, 2018) were supported by the future probing practices and seemed to create space for employees to engage in exploration. There was still a lack of compelling acts of brokering and network cohesion (Uhl-Bien & Arena, 2018). This may indicate why linking back to daily practice is challenging. We assume that organising for adaptability requires a deliberate act of connecting far future explorations with present action, and propose that besides explorative skills, ‘adaptive anticipating’ action is needed to make the connection and that linking back through near future experiments might be a way to achieve this.
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We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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